Loss aversion is one of the most widely used concepts in behavioral economics. We conduct a large-scale interdisciplinary meta-analysis, to systematically accumulate knowledge from numerous empirical estimates of the loss aversion coefficient reported during the past couple of decades. We...

We introduce DOSE - Dynamically Optimized Sequential Experimentation - and use it to estimate individual-level loss aversion in a representative sample of the U.S. population (N=2,000). DOSE elicitations are more accurate, more stable across time, and faster to administer than standard methods....

One of the central predictions of the life cycle hypothesis is that individuals smooth consumption over their economic life cycle; thus, they save when income is high, in order to provide for when income is likely to be low, such as after retirement. We test this prediction in a group of...

We use neural data collected from an experimental asset market to measure regret preferences while subjects trade stocks. When subjects observe a positive return for a stock they chose not to purchase, a regret signal is observed in an area of the brain that is commonly active during reward...

One of the central predictions of the life-cycle hypothesis is that individuals smooth consumption over their economic life cycle; thus, they save when income is high, in order to provide for when income is likely to be low, such as after retirement. We test this prediction in a group of people...

Neuroeconomics shares the main goals of microeconomics: to understand what causes choices, and the welfare properties of choice. The novel goal is linking mathematical constructs and observable behavior to mechanistic details of neural circuitry. Several complementary methods are used. An...

Sixteen subjects' brain activity were scanned using FMRI as they made choices, expressed beliefs, and expressed iterated 2nd-order beliefs (what they think others believe they will do) in eight games. Cingulate cortex and prefrontal areas (active in theory of mind and social reasoning) are...

We apply a machine-learning algorithm calibrated from general human vision to predict visual salience of parts of a stock price series. We hypothesize that visual salience of adjacent prices increases decision weights on returns computed from those prices. We analyze the inferred decision impact...